研究扩散策略如何学习机器人运动约束，发现数据量和质量影响更大。
How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
- 用扩散模型学习双臂抓取任务中的运动约束，分析数据规模、质量与曲率影响。
- 数据量或质量下降时，约束学习效果明显变差，但曲率影响不显著。
- 实验结果在真实机器人上验证，对实际应用有指导意义。
扩散策略在机器人模仿学习中表现优异，即使面对需要满足运动学等式约束的任务。然而，任务表现并非衡量策略是否精确学习训练数据中约束的可靠指标。为此，我们通过双臂抓取任务案例研究扩散策略对运动学约束流形的学习能力，考察数据集规模、数据质量及流形曲率三个因素的影响。实验表明，扩散策略仅能学习到约束流形的粗略近似，且其学习效果受数据集规模和质量下降的负面影响。而约束流形曲率与约束满足率及任务成功率之间关系不明确。硬件测试验证了结论在现实世界中的适用性。项目网站包含更多结果与可视化：https://diffusion-learns-kinematic.github.io
原文摘要 · Abstract (English)
Diffusion policies have shown impressive results in robot imitation learning, even for tasks that require satisfaction of kinematic equality constraints. However, task performance alone is not a reliable indicator of the policy's ability to precisely learn constraints in the training data. To investigate, we analyze how well diffusion policies discover these manifolds with a case study on a bimanual pick-and-place task that encourages fulfillment of a kinematic constraint for success. We study how three factors affect trained policies: dataset size, dataset quality, and manifold curvature. Our experiments show diffusion policies learn a coarse approximation of the constraint manifold with learning affected negatively by decreases in both dataset size and quality. On the other hand, the curvature of the constraint manifold showed inconclusive correlations with both constraint satisfaction and task success. A hardware evaluation verifies the applicability of our results in the real world. Project website with additional results and visuals: https://diffusion-learns-kinematic.github.io
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